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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css"
integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous">
<link rel="stylesheet" href="styles.css">
<!-- JQuery -->
<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<!-- Bootstrap tooltips -->
<script type="text/javascript"
src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.4/umd/popper.min.js"></script>
<!-- Bootstrap core JavaScript -->
<script type="text/javascript"
src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.5.0/js/bootstrap.min.js"></script>
<!-- MDB core JavaScript -->
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src="https://cdnjs.cloudflare.com/ajax/libs/mdbootstrap/4.19.1/js/mdb.min.js"></script>
<!-- Font Awesome -->
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<!-- Google Fonts -->
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700&display=swap">
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<!-- Material Design Bootstrap -->
<link href="https://cdnjs.cloudflare.com/ajax/libs/mdbootstrap/4.19.1/css/mdb.min.css" rel="stylesheet">
<title>REPORT</title>
</head>
<body>
<div class="container">
<h4>16S rRNA WORKFLOW REPORT</h4>
<div class="intro">
<h5>INTRODUCTION</h5>
<p> The 16S rRNA is a ribosomal RNA necessary for the synthesis of all prokaryotic proteins. The genes
coding for the 16S rRna has regions that are highly conserved and are shared
in all bacteria species making it useful in identifying bacteria in samples. Moreover,
the gene has variable regions which can be used to reconstruct phylogenies.
</p>
</div>
<div>
<p>In this miniproject we created two worklows that can be used to determine microbial communities present in
different enivironment using the 16S rRNA analysis.</p>
</div>
<div class="obj">
<h3>Objectives.</h3>
<ol>
<li>To develop a 16S-rRNA analysis workflow.</li>
<li>To test the pipelines developed.</li>
<li>To analyse the results.</li>
<li>To compare the tools used.</li>
</ol>
</div>
<div id="quiz1">
<h4>Input Data Assessment</h4>
<ul>
<li>The input datasets were of good quality based on the FASTQ results</li>
<div class="row">
<div class="col-md-6">
<img src="images/fastq_before.png" alt="">
<p style="text-align: center;">Before Trimmomatic</p>
</div>
<div class="col-md-6">
<img src="images/trimmomaticsfastq.png" alt="">
<p style="text-align: center;">After Trimmomatic</p>
</div>
<p>The reads before trimming had an error rate (EE) mean of 3.5 (R2) compared to forward reads which had an E.E mean of about 0.5. The error rate mean reduced to 0.2 for R1 and 0.7 for R2. </p>
</div>
</ul>
</div>
<p>The flow charts below show tools used on the workflows in three phases namely:</p>
<ol>
<li>Preprocessing Reads.</li>
<li>OTU picking,Classification and Phylogenetic Tree Generation.</li>
<li>Measure Diversity and other Statistical Analyses.</li>
</ol>
<div class="row">
<div class="col-md-6">
<h4>QIIME Workflow</h4>
<img src="images/oo.svg" style=" border: 5px solid #555; height: 800px;" alt="qiime workflow">
<h5>Runtime Analysis</h5>
<ul>
<li>Time Taken: 106mins</li>
<li>Hardware Usage- Fair ,user can perform other light tasks while running.</li>
<li>Storage - 8.9 G.B </li>
</ul>
</div>
<div class="col-md-6">
<h4>Multiple-Tools Workflow</h4>
<img src="images/uparse.png" style=" border: 5px solid #555; height: 800px;" alt="uparse/uchime">
<h5>Runtime Analysis</h5>
<ul>
<li>Time Taken - 2.5 hrs.</li>
<li>Hardware Usage - Intensive,computer freezes.</li>
<li>Storage - 8.8 G.B</li>
</ul>
</div>
</div>
<div id="quiz2">
<h4>Operational Assessment</h4>
<h5>Factors considered </h5>
<ul>
<li>Speed</li>
<li>Accuracy</li>
<li>Cost</li>
<li>Output Formats and Reports</li>
<li>Hardware Requirements</li>
</ul>
</div>
<h5>Alternative Tools Critique</h5>
<ol>
<li>Trimmomatic vs Prinseq: Trimmomatic improved quality after trimming but prinseq did not trim anything.</li>
<li>UPARSE (98% merged) vs PEAR(99% merged): UPARSE does not require input of reverse reads unlike PEAR. PEAR
produces 4 outputs.</li>
<li>UCHIME vs Chimeraslayer: Uchime is faster and detects more chimeras than Chimeraslayer which also requires
different formats.</li>
<li>UPARSE vs QIIME : Qiime picked 209 OTUS while UPARSE had 190 OTUS.DADA2 pipeline picked 314 ASVs. </li>
<li>UCLUST vs QIIME: UCLUST uses less time than QIIME. We opted to use UPARSE</li>
<li>QIIME vs Mothur : Mothur deletes all reads in the preparation stage whereas QIIME picked 209 OTUS using
deblur plugin.</li>
<a
href="https://training.galaxyproject.org/archive/2019-02-07/topics/metagenomics/tutorials/mothur-miseq-sop/tutorial.html">Mothur
Tutorial</a>
<li>MAFFT vs pyNAST for Alignment: Couldnt download pyNAST cause it requires python version 2.7 and below.</li>
</ol>
<h5>Results Analysis/Metrics Comparions</h5>
<p>Comparison of the Alpha and Beta diversity analyses from the two workflows.</p>
<div class="row">
<div class="col-md-6">
<img src="images/alpha-compare (3).svg" alt="qiime shannon">
<p> Alpha Diversity Shannon Index(q.w)</p>
<a href="images/alpha-compare (3).svg" target="_blank">Link to image</a>
</div>
<div class="col-md-6">
<img src="images/alpha-compare (2).svg" alt="shannon">
<div class="bottom-left">Alpha Diversity Shannon Index(o.w)</div>
<a href="images/alpha-compare (2).svg" target="_blank">Link to image</a>
</div>
</div>
<div class="row">
<div class="col-md-6">
<img src="images/emperor (11).png" alt="jaccard">
<div class="bottom-left">Bray Curtis Emperor(q.w)</div>
<a href="images/emperor (11).png" target="_blank">Link to image</a>
</div>
<div class="col-md-6">
<img src="images/emperor (10).png" alt="jaccard">
<div class="bottom-left">Bray Curtis Emperor(o.w)</div>
<a href="images/emperor (10).png" target="_blank">Link to image</a>
</div>
</div>
<div class="container my-4">
<p><b>Other Images from Qiime workflow....</b></p>
<a class="btn btn-green" href="images/level-7-bars (1).svg">Taxonomy Barplot</a>
<a class="btn btn-indigo" href="images/alpha-rarefaction.png">Alpha rarefaction/Shannon</a>
<a class="btn btn-purple" href="images/rareobserved.png">Alpha rarefaction/Observed otus</a>
<a class="btn btn-dark-green" href="images/deblur-stats.png">Deblur Stats</a>
<a class="btn btn-purple" href="images/tree (1).svg">Rooted Tree</a>
<a class="btn btn-primary" href="images/tree (2).svg">Unrooted Tree</a>
<a class="btn btn-purple" href="images/bVc7ka1Dm3yUi2DczboC1g.svg">Circular Tree</a>
<a class="btn btn-primary" href="images/metadata (2).tsv">Taxonomy Table</a>
<a class="btn btn-dark-green" href="images/unweighted_unifrac.png">Unifrac Unweighted</a>
<a class="btn btn-indigo" href="images/emperor (12).png">Unweighted unifrac/Emperor</a>
</div>
<footer>
<h4>Summary</h4>
<ul style="font-size: 0.9rem;"><strong>
<li>Quality Filtered -Good.</li>
<li>Trimmed-Not many reads were trimmed(roughly 24-40).</li>
<li>99% stitched with pear.</li>
<li>43% Chimeras detected.</li>
<li>SILVA Database vs Greengeens : Silva is wide and more curated than greengeens.</li>
<li>216 and 209 OTUS picked from USEARCH and Qiime respectively.</li>
<li>Rarefaction sequence depth- G is most dense followed by K then B.But the curve flattened.</li>
<li>OTU correlated with any treatments or metadata - B is the most diverse(Least effective treatment cause has diverse species) followed by K
then G.</li>
<p style="font-weight: bolder;">G is the most effective treatment followed by K then B is the least effective.</p>
</strong>
</ul>
<div id="conc"><strong>
<h4>Conclusion</h4>
<p>1.QIIME end to end is better - all factors considered.</p>
<p>2.Generally, the two pipelines show comparative results in terms of how the different treatments affected the samples.
<p>3.There was a slight disperity in species density and diversity which is attributed to the OTU picking method used by pipelines.</p>
<p>4.The quality of reads is paramount. </p>
</strong>
</div>
<h3 style="text-align: center;">THE END!!!</h3>
</footer>
</div>
</body>
</html>